Monitoring for invalid data from field instruments

ABSTRACT

Processing systems and methods used to identify faults in the operation of one or more field instruments are described and shown. In one embodiment, a sensing system includes one or more field instruments and a processing component configured to process data from the field instruments. This processing may include identifying invalid samples of data using an algorithm; correlating invalid samples of data to specific field instruments; and determining a likelihood of a fault occurring on the specific field instruments. The processing may occur in real-time using an online processing technique, or with an offline processing technique on data maintained in a data store.

BACKGROUND

Sensor instruments and systems are deployed in a variety of consumer,industrial, and commercial applications. As an example of sensordeployment in a common industrial setting, tank farms and tank terminalsprovide a series of storage tanks containing various products (e.g.petrochemicals) that pass through different stages of a processingworkflow. High accuracy and reliability of field instruments (e.g. levelsensors and temperature sensors) is needed for precise volumecalculations in these storage tanks. However, field instruments may,under certain conditions, generate invalid data, such as data jumps,out-of-range data, excessive variance (noise), frozen readings, missingdata, and the like. Invalid sensor readings may pose a safety issue andmay also cause financial losses if undetected.

Invalid or inaccurate sensor data may be caused by a variety of factors,including: sensor fault (mechanical or electrical) initiated by variousdamage mechanisms (e.g. water ingress), power outages, communicationerrors, incorrect value of instrument parameters, and the like. Althoughsome field instruments may already be equipped with some level ofon-board diagnostic functionality, the existing diagnostic algorithmsdeployed in sensors and in sensor monitoring systems may not be able todetect all cases of invalid data.

SUMMARY

Various processing systems and techniques are described herein thatenable the recognition of invalid data and faults occurring in fieldinstruments. In one embodiment, a sensing system includes a fieldinstrument and a processing component coupled to the field instrument.The processing component may be configured to process data from thefield instrument in real-time using online data processing techniquesand systems, or at later times using offline data processing techniquesand systems. Invalid samples of data from the field instruments may beidentified using a filtering algorithm. From the results of thefiltering algorithm, a likelihood of a fault occurring in the fieldinstrument may be determined.

In further embodiments, the processing component may be configured tomonitor multiple field instruments and correlate invalid samples of thedata to one or more specific field instruments from the multiple fieldinstruments. The likelihood of a fault occurring in the field instrumentmay be further interpreted to provide transmission of a warning, errormessage, or other information about the field instrument to anappropriate user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example embodiment of a monitoring system connected toa plurality of field instruments in an illustrative tank farmconfiguration;

FIG. 2 depicts an example embodiment of a monitoring system connected toa plurality of field instruments in an illustrative tank configuration;

FIGS. 3A, 3B, and 3C depict example graphs of product level, producttemperature, and product temperature status indicating of data jumpsproduced by field instruments monitored in connection with an exampleembodiment of a monitoring system;

FIGS. 4A and 4B depict example graphs of a filtering algorithmrecognizing data jumps produced by field instruments monitored inconnection with an example embodiment of a monitoring system;

FIG. 5 depicts an example block diagram of a series of data processingsteps performed in connection with an example embodiment of a monitoringsystem; and

FIG. 6 depicts a block diagram of an example computer system thatperforms data analysis from various sensors in connection with anexample embodiment of a monitoring system.

DETAILED DESCRIPTION

In the following description, reference is made to the accompanyingdrawings that form a part hereof, and in which is shown by way ofillustration specific embodiments that may be practiced. Theseembodiments are described in sufficient detail to enable those skilledin the art to practice the invention, and it is to be understood thatother embodiments may be utilized and that structural, logical, andelectrical changes may be made without departing from the scope of thepresent invention. The following description of example embodiments is,therefore, not to be taken in a limited sense, and the scope of thepresent invention is defined by the appended claims.

The functions or algorithms described herein may be implemented insoftware or a combination of software and human or enterpriseimplemented procedures in one embodiment. The software may consist ofcomputer executable instructions stored on computer readable media suchas memory or other type of storage devices. Further, such functionscorrespond to modules, which are software, hardware, firmware, or anycombination thereof. Multiple functions may be performed in one or moremodules as desired, and the embodiments described are merely examples.The software may be executed on a digital signal processor,application-specific integrated circuit (ASIC), microprocessor, or othertype of processor operating on a computer system, such as a personalcomputer (PC), server or other computer system.

A series of techniques and systems disclosed herein enable enhancedmonitoring of measured data from field instruments, and may be deployedto detect and prevent problems and faults originating from invalid data.In one embodiment, a monitoring system is configured to collect orprocess data in connection with a product being evaluated by a pluralityof field instruments. Such field instruments include, but are notlimited to, gauges, sensors, or probes. The monitoring system may beconfigured to process the data and automatically detect or recognize anyabnormal events or faults from the data values of the various fieldinstruments.

The monitoring system may then perform additional filtering steps,rule-based reasoning, correlations, and indications in connection withthe invalid data processing steps. Further, a likelihood of a faultoccurring in a specific field instrument may be determined from anabnormal set of data. Upon processing and the determination of abnormalevents, invalid data, or specific faults, appropriate action, such asalerts and automated corrections, may be performed.

The following describes a number of algorithms and techniques deployedin real-world examples with storage tanks and tank farms. It should beunderstood that the algorithms and techniques described herein wouldapply to a variety of configurations for sensor systems and performing adetection of invalid data generated by field instruments and other typesof sensors generally. Therefore use of the presently describedembodiments in a tank farm or other product storage setting is onlyprovided as a specific example, and is not intended to limit the widerscope or applicability of the invention to other types of fieldinstruments that do not involve tanks or product storage monitoring.

FIG. 1 illustrates an example monitoring system 100 deployed in a tankfarm, the tank farm providing storage facilities 111, 112, 113, 114 fora set of products 121, 122, 123, 124 viewable in cut out portions of therespective storage facilities. The tank farm is configured to have themonitoring system 100 actively monitor the sets of products 121, 122,123, 124 in each facility using a plurality of field instruments 131,132, 133, 134 connected to a data collection or processing unit 140 vianetwork 141.

The processing unit 140 may be configured to implement analysis of datacollected from the field instruments with an online algorithm 150, ormay collect the data for later processing with an offline algorithm 160by the storage of archived data 164 in database 162. The processing unit140 may be further connected to a management user interface, such as maybe provided in computer system 145 via network connection 142. Themanagement user interface may enable real-time or delayed usermonitoring and control of the variety of components presented in tankfarm monitoring system 100.

The field instruments 131, 132, 133, 134 deployed in the tank farmmonitoring system 100 include a product level gauge (e.g., a radar gaugeor servo gauge), and various sensors, probes, and associated datatransmitters to monitor storage conditions for products 121, 122, 123,124. In the tank farm industry, the term “product” generally refers tothe contents of the storage tank. As various examples of product beingmonitored in a tank farm setting, product may include stored liquid orgas, such as crude oil, jet fuel, liquid natural gas, and the like. Keymeasured storage variables for liquid or gas product are product level,product temperature, product pressure, product flow, product density,vapor temperature, vapor pressure, water level, and the like.

A field instrument may comprise a gauge, sensor, or probe, although manyother types of instruments and monitoring units may be used. In oneembodiment, a field instrument simply measures some physical variable(hence it is a sensor); but various types of field instruments may beconfigured with increasing complexity to also perform computations,generate error codes, and receive commands to perform certain actions oranalysis. Therefore, the field instrument may include a wide level ofintegrated processing, or may provide raw data for analysis in a largermonitoring system.

A field instrument and its associated data transmitter may include oneor more sets of sensors. For example, a temperature probe placed in astorage tank may consist of multiple vertically arranged temperatureelements, and may optionally include a water level probe for placementnear the bottom of the tank. Some of the data measured by fieldinstruments might include product level (by radar or servo gauge);storage pressure conditions; product temperature (by temperature probe);vapor temperature (by temperature probe); product density (by servogauge); water level (by water level probe); and external datapotentially relevant to the monitored product (e.g. weather conditionssuch as ambient temperature, ambient humidity, barometric pressure, andwind speed external to the storage location).

FIG. 2 provides an illustration of a deployment of a plurality of fieldinstruments in a cross-section of a petrochemical storage tank 200. Asillustrated, a probe 210 used in tank 200 consists of sets of multipletemperature elements 211, 212 and a water probe 213 located in avertical column and distributed uniformly at specific heights from thetank bottom to the tank top. The probe 210 may provide a data interface215 that is connected to other field instruments (such as level gauge251) or external data monitoring systems (not shown).

The product provided within tank 200 has separated into vapor 221,product (liquid) 222, and water 223. Other field instruments monitoringthe tank may include pressure transmitter 231 for monitoring productpressure provided by hydrostatic pressure, pressure transmitter 232 formonitoring vapor pressure, and level gauge 251 for monitoring the levelof product within the tank 200. Other field instruments such as aflowmeter may be integrated with the tank, or located internally orexternally to the tank. As another example, field instruments externalto the tank 200 might include sensors for wind speed, ambienttemperature, ambient humidity, and barometric pressure.

As previously mentioned, the temperature probe 210 includes a pluralityof sets of temperature elements 211, 212, 213. The temperature elements211 are not immersed in the illustrated state of the tank but providetemperature monitoring for the vapor portion of the tank, whereastemperature elements 212 have become immersed in liquid and provide atemperature indication of the product 222 at the various liquid levelsof the tank. Further, water probe 213 residing near the bottom of thetank will monitor the level of the water contents, which have becomeseparated from the product 222.

As a typical monitoring example, the individual sensor readings such astemperatures from individual elements within temperature probe 210 maybe averaged to obtain the average product temperature (computed usingdata from submerged elements) and average vapor temperature (computedusing data from non-submerged elements) for the tank. The resultingaverage values may then be archived in a supervisory softwareapplication, where a typical logging period is between 1 and 15 minutes.The archiving process is applied also to other types of measured andcalculated data, such as product level, water level, product volume,product mass, and the like.

Once the data is collected from the field instruments, various anomaliesmay be detected from this data. This may be used for the purpose ofdetermining if a particular sensor is providing abnormal data (even ifthe sensor reports a normal status). In one embodiment, the monitoringsystem seeks to perform the monitoring of a plurality of fieldInstruments with a mathematical algorithm and fault detection techniquesto process and extract useful data points and analysis from the data.

FIGS. 3A, 3B, 3C depict an illustration of three correlated sets of datacollected from a monitoring system, including product level 310 in FIG.3A, product temperature 320 in FIG. 3B, and product temperature sensorstatus 330 in FIG. 3C over a period of time. The product temperaturestatus indicates a reportable operating condition of the sensor 332,with status 00 indicating normal operation throughout the period ofmonitoring. Thus, the product temperature sensor appears at first viewto be operating normally.

As shown in the product level measurement 310, the product level 312 hasgradually increased over a period of time without any fluctuation.However, the product temperature 320 has experienced a number offluctuations in the temperature measurement 321, as shown with a varietyof dramatic drops at data points 322, 323, 324, 325, 326, 327, and 328.

Given the known quantities of the petrochemical product, the gradualproduct level changes, and the sensor status, these drops may bedetermined as the result of an incorrect sensor configuration, such asincorrect parameters programmed to the sensor. If these data anomaliesare detected, a user can be alerted to the incorrect operation of theappropriate field instrument, and corrections or replacements can bemade in connection with the temperature sensor or appropriate storageequipment.

The following describes specific algorithms applied to filter the datameasured by a field instrument, with the goal to automatically detectany abnormal event or data anomaly originating from a field instrument.An abnormal event originating from a field instrument may indicate afault, but not all abnormal events are faults. Some examples of abnormalevents include no data, a data jump (e.g., a temperature jump, leveljump, or other rapid change exceeding a maximum limit, either in apositive or negative direction), data with an abnormally low numericvalue (e.g. a too-low product temperature relative to the properties ofthe product, or a too-low product level, such as a negative productlevel), data with an abnormally high numeric value (e.g., a too-highproduct temperature for the properties of the product, too-high vaportemperature, too-high product pressure, too-high vapor pressure, and thelike), and noisy data.

In a specific example of a filtering algorithm, the original data isfirst smoothed (low-pass filtered) (e.g., using an EWMA (ExponentiallyWeighted Moving Average) filter with a specific value of forgettingfactor (smoothing constant). Second, a high-pass filtered data isobtained by subtracting the low-pass filtered data from the originaldata. Third, the high-pass filtered data is used to compute the meanvalue M and standard deviation S. Fourth, the upper control limit (UCL)and lower control limit (LCL) are computed as M+(3*S) and M−(3*S),respectively. Fifth, any sample of the high-pass filtered data whosevalue (amplitude) is greater than the UCL or lower than the LCL isregarded as invalid data.

FIGS. 4A and 4B provide a graphical illustration of data jump filteringused in connection with a presently described embodiment. First, asshown in product temperature graph 410 in FIG. 4A, the producttemperature measurement 412 has a variety of significant changesoccurring approximately at times 20.8, 21.05, and 21.2. An exponentiallyweighted moving average of this data 414 (by using λ₁=0.95) may becomputed.

As shown in filtered signal graph 420 in FIG. 4B, the filteredtemperature measurement 422 (i.e., the high-pass filtered data) iscompared with an upper control limit 424A and a lower control limit424B. As illustrated, at three points 432, 434, 436, the filtered signalexceeds the control limits. The data then may be recognized as invalidat points 432, 434, 436.

A variety of empirical rules may also be performed in connection withthe previously described algorithm to provide more accurate or morefocused indications of invalid data samples. In one embodiment, a datajump detection algorithm may require satisfaction of a set of empiricalrules, such as an empirical rule requiring the difference between twosuccessive samples of raw data to be greater than a deadband parameterD. For example, given algorithm parameters deadband D (sensor-specific),sensitivity K (e.g., 3), maximum variance threshold T₁ (e.g., 3),maximum kurtosis threshold T₂ (e.g., 6), and forgetting factors λ₁, λ₂,λ₃, λ₄, λ₅ (where forgetting factor λ is between 0 and 1, e.g. 0.95,0.995, etc.), the following computational model may be used:

-   -   1. z[k]=F{x[k]}, where a filtering operation F{ } can be        represented by:        z[k]=x[k]−μ_(x)[k]=x[k]−(λ₁μ_(x)[k−1]+(1−λ₁)·x[k]) or by:        z[k]=x[k]−x[k−1], or by other filtering method.    -   2. μ_(z)[k]=λ₂·μ₂[k−1]+(1−λ₂)·z[k]    -   3. σ_(z) ²[k]=λ₃·σ_(z) ²[k−1]+(1−λ₃)·(z[k]−μ_(z)[k])²    -   4. U[k]=μ_(z)[k]+K·√{square root over (σ_(z) ²[k])}    -   5. L[k]=μ_(z)[k]−K·√{square root over (σ_(z) ²[k])}    -   6. σ_(x) ²[k]=λ₄·σ_(x) ²[k−1]+(1−λ₄)·(x[k]−μ_(x)[k])²    -   7. β_(x)[k]=λ₅·β_(x)[k−1]+(1−λ₅)·[(x[k]−μ_(x)[k])⁴/(σ_(x)        ²[k])²]

The following nomenclature may be used: k is the discrete time (datasample index), x[k] is the k-th sample of the data, x[k−1] is the(k−1)-th sample of the data, z[k] is the k-th sample of the filtereddata, F{ } is a filtering operation, μ_(x) is the mean value of x, μ_(z)is the mean value of z, σ² _(x) is the variance of x, σ² _(z) is thevariance of z, U[k] is the k-th sample of the upper control limit, L[k]is the k-th sample of the lower control limit, and β_(x)[k] is thekurtosis of x.

The reasoning model for operation of the data jump detection algorithmmay operate as follows:

-   -   If z[k]≧U[k] or z[k]≦L[k], then R₁ is true;    -   If |x[k]−x[k−1]|≧D, then R₂ is true;    -   If R₁ is true and R₂ is true, then x[k] is regarded as an        invalid sample due to a presence of a data jump at the sample        x[k].

The reasoning model for operation of the noisy data detection algorithmmay operate as follows:

-   -   If σ_(x) ²[x]≧T₁, then R₃ is true;    -   If β_(x)[k]≧T₂, then R₄ is true;    -   If R₃ is true or R₄ is true, then x[k] is regarded as an invalid        sample due to noisy data occurring at the sample x[k].

Stored statistics in memory may be used in the next iteration of thealgorithm:

x[k], μ_(x)[k], μ_(z)[k], σ_(x) ²[k], σ_(z) ²[k], β_(x)[k]

Additionally, when x[k] is missing (no data), then:

μ_(x) [k]=μ _(x) [k−1]

μ_(z) [k]=μ _(z) [k−1]

σ_(x) ² [k]=σ _(x) ² [k−1]

σ_(z) ² [k]=σ _(z) ² [k−1]

β_(x) [k]=β _(x) [k−1]

The algorithm is initialized (at the sample time k=1) using thefollowing constants:

μ_(x)[1]=0

μ_(z)[1]=0

σ_(x) ²[1]=0

σ_(z) ²[1]=0

β_(x)[1]=0

The previously described high-pass and low-pass filtering operation isonly one example of an applicable filter used in the algorithm. Thefilter utilized in the filtering operation may be a combination of oneor more frequency filters (e.g., a low-pass, high-pass, band-pass, orband-stop filter), non-linear filters (e.g., a median-based, orpercentile-based filter), a weighted averaging filter, a wavelet filter,a frequency-domain thresholding filter, a wavelet-domain thresholdingfilter, a cepstrum-domain thresholding filter, an adaptive linearfilter, an adaptive non-linear filter, or any other linear or non-linearfilter with either fixed or adaptive filter coefficients implementedusing either recurrent or block-wise computation.

The previously described mean value, variance, and kurtosis are onlyexamples of applicable statistical indicators used in the algorithm. Thestatistical indicator may be a combination of one or more statisticalindicators, and may involve a higher-order moment, a statisticaldistance, a correlation coefficient, information entropy, and the like.

The workflow of algorithmic steps, as well as the previously describedsteps of detecting invalid data and invalid data changes generated by abroad range of sensors, provides the ability to detect and respond toinvalid and anomalous data points. FIG. 5 provides an illustration of aseries of data processing steps 500 performed in connection with aproduct temperature field instrument, used to produce an overall probecondition that factors the fault likelihood of the instrument and otherrelevant data inputs. The same general detection mechanism may be usedfor various sensor types, with fine-tuning to various sensor or producttypes with specific parameters as appropriate.

As shown in FIG. 5, product temperature and product temperature status510 are provided as input to a series of data processing checks, (thedetection of temperature jumps 514 and temperature range checking 516).If the temperature jumps are significant, or the temperature is out of anormal range, then this information may be used to identify anomalousdata from a specific field instrument. As input to verification of thetemperature range 516, minimum and maximum product temperaturethresholds may be computed 518. These range computations may originatebased on the type of product, the sensor operating limits, ASTM tables,and optional factors such as an insulation factor and ambienttemperature. Likewise, as input to the determination of data jumpalgorithm parameters 512, parameters such as a sampling period, sensorinformation, and product information may be factored to determine aminimum temperature change ΔT_(min), which represents an example of thedeadband parameter D in case of temperature sensors.

Further processing may be performed in connection with data jumpdetections, such as an operation for factoring or interpreting datastatuses 520. This may include checking the status of the fieldinstrument, such as product temperature status or other data statuses,against a status dictionary to determine if the sensor was reporting anormal status. Other processing 522, such as assigning a temperaturejump to the currently active element (such as a specific thermoelementin an element array), may be performed based on the correlation to themeasured product level and other parameters and data points. Forexample, relevant parameters may include product immersion depth (e.g.500 mm), gas immersion depth (e.g. 500 mm), temperature element offset(e.g. 300 mm), switch hysteresis (e.g. 100 mm), and the like.

Based on the various detections 514, 520, 522 (or, alternately, thetemperature range detection 516), the fault likelihood of a specificfield instrument may be provided or updated 524. The fault likelihooddetermination may factor ΔT_(min) (minimum temperature change), ΔT_(max)(maximum temperature change), and ΔF_(max), (maximum fault likelihoodchange). Remedial action may be undertaken based on a predeterminedthreshold of fault or the change of the fault likelihood. Based on thelikelihood of fault, a generation of an overall probe condition statusupdate 526 and associated processing may occur.

The presence of invalid data may be reported to the user (e.g., tankowner, service engineer, maintenance technician, etc.) by appropriatemeans (e.g., error message, specific data status, etc). The analysisprovided by the diagnostic system may also support sensor maintenancedecisions by providing a prioritized list of sensors, such as beingordered by total number of detected anomalies, severity of worstdetected anomaly, minimum/maximum sensor reading, and the like.

In further embodiments, the presently disclosed monitoring system canidentify a faulty sensor within a field instrument comprising an arrayof sensors. For example, the monitoring system may identify a faultytemperature element in an array of temperature elements by identifyingand assigning a detected temperature data jump to a specific activetemperature element. This may include factoring the measured productlevel and known positions of the temperature elements to derive whichelement is producing faulty data.

In still further embodiments, detected anomalies from multiple sensorsin a single tank may be synchronized in time. For example, if the tankis shaking, more sensors within the tank are likely to generateanomalies simultaneously. Also, detected anomalies from multiple tanksin proximity to each other may be synchronized in time. For example, incase of power failure, an anomaly may be detected as occurring at thesame time in multiple tanks.

A variety of other improvements may be provided to the presentlydisclosed techniques to enable correlation of complex instrument faultand error settings. This includes techniques for correlating thedetected level jumps with measured wind speed in order to reject falsealarms (i.e., level jumps caused by excessive wind or weather conditionsinstead of a sensor fault). Level jumps detected by an algorithm alsomay be synchronized in time with measured wind speed or other weatherconditions; for example, if the wind speed is too high, the detectedlevel jump is regarded as correct behavior, not originating from asensor fault, but the result of product movement.

Other correlation improvements may include detecting a faultytemperature probe by correlating measured product temperature withambient temperature and detecting significant unexpected changes ofproduct temperature. Still other correlation improvements may includedetecting a faulty temperature probe by comparing the measured producttemperature with expected temperature limits of the stored product.Expected temperature limits may be obtained either from prior physicalknowledge (e.g. boiling temperature, melting temperature, flashtemperature, auto-ignition temperature, and the like) or by statisticalanalysis of historical data (determining normal temperature limits fromdata measured by a fault-free temperature probe). A similar mechanismmay be used for vapor temperature and expected vapor temperature limits.

A combination of the previously described techniques, and the algorithmto detect invalid sensor data, may be employed in online and offlineprocessing scenarios. First, an online version of the algorithm may beintended for running in a hardware device (e.g., an interface unitcommunicating with field instruments and transmitting the data into asupervisory software application). In this case (online version), thealgorithm may be optimized for processing the data on thesample-by-sample basis.

Alternately, the offline version of the algorithm can run in a personalcomputer or other computing device as part of a software application toanalyze the archived historical datasets. For example, this would beuseful to analyze data collected from multiple tanks over severalmonths. In this case, the algorithm may be optimized for processing thedata on a batch basis.

The presently disclosed techniques may also be incorporated into alarger condition based maintenance (CBM) or site device monitoring (SDM)system that is intended for monitoring a plurality of field instrumentsfrom a plurality of subsystems and sites. For example, in connectionwith a monitoring engine running on a hardware platform, the presentlydescribed filtering algorithm may perform various diagnostic tasks toautomatically detect abnormal behavior in specific field instruments orin subsystems having multiple field instruments. This hardware platformmay be responsible for other processing tasks, such as field scanning,inventory calculations, alarming, and the like. Identified faults andother relevant information regarding the detection of faults from fieldinstruments may be provided to various analytical components in the CBMor SDM system, as data is further processed, communicated, and evenvisualized to end users, as appropriate. The presently disclosedfiltering and detection algorithms and techniques may be used as areplacement for, or in conjunction to, existing CBM anomaly detectionand processing techniques.

A block diagram of an example computer system that performs dataanalysis from various sensors, and is configured to implement thepresently described algorithm (in either the online or offlinescenarios) and execute other programming, is shown in FIG. 6. A generalcomputing device in the form of a computer 610 may include a processingunit 602, memory 604, removable storage 612, and non-removable storage614. Memory 604 may include volatile memory 606 and non-volatile memory608. Computer 610 may include, or have access to a computing environmentthat includes, a variety of computer-readable media, such as volatilememory 606 and non-volatile memory 608, removable storage 612 andnon-removable storage 614. Computer storage includes random accessmemory (RAM), read only memory (ROM), erasable programmable read-onlymemory (EPROM) and electrically erasable programmable read-only memory(EEPROM), flash memory or other memory technologies, compact discread-only memory (CD-ROM), Digital Versatile Disks (DVDs) or otheroptical disk storage, magnetic cassettes, magnetic tapes, magnetic diskstorage or other magnetic storage devices, or any other medium capableof storing computer-readable instructions. Computer 610 may include orhave access to a computing environment that includes input 616, output618, and a communication connection 620. The computer may operate in anetworked environment using a communication connection to connect to oneor more remote computers. The remote computer may include a personalcomputer (PC, server, router, network PC, a peer device or other commonnetwork node, or the like. The communication connection may include aLocal Area Network (LAN), a Wide Area Network (WAN) or other networks.

Computer-readable instructions to execute methods and algorithmsdescribed above may be stored on a computer-readable medium, such asillustrated at a program storage device 625, are executable by theprocessing unit 602 of the computer 610. A hard drive, CD-ROM, and RAMare some examples of articles including a computer-readable medium. Inone embodiment, a user interface is provided, such as a touchscreendevice for providing both input 616 and output 618.

Although the previously described embodiments are described withreference to specific sensor deployment settings, those skilled in theart would recognize that the presently described techniques may bedeployed in a number of settings to process and detect invalid data.

1. A sensing system comprising: a field instrument; a processingcomponent operably coupled to the field instrument and configured toidentify faults in operation of the field instrument, by: processingdata collected from the field instrument; identifying an invalid sampleof the data using an algorithm, the algorithm comprising: performing afiltering operation on the data to generate filtered data; computing amean value M and standard deviation S from the filtered data; computingan upper control limit and lower control limit using the mean value Mand standard deviation S; and identifying the invalid sample from thefiltered data, the invalid sample providing one or more values greaterthan the upper control limit or lower than the lower control limit andsatisfying a set of one or more empirical rules; and determining alikelihood of a fault occurring in the field instrument from the invalidsample of the data.
 2. The system of claim 1, further comprising: aplurality of additional field instruments; wherein the processingcomponent is further configured to identify faults in operation of thefield instrument or the plurality of additional field instruments by:processing the data collected from the field instrument and theplurality of additional field instruments using an online process inreal time; and correlating the invalid sample of the data to one or morespecific field instruments.
 3. The system of claim 1, wherein the fieldinstrument is selected from the group consisting of a product levelgauge, temperature probe, water level probe, pressure transmitter, andflowmeter.
 4. The system of claim 1, wherein the data measured by thefield instrument includes one or more of product level, producttemperature, product pressure, product flow, vapor temperature, vaporpressure, product density, water level, and weather data.
 5. The systemof claim 1, wherein the field instrument comprises an array of fieldinstrument elements, and wherein the processing component is furtherconfigured for identifying a detected change and the likelihood of faultfor a specific element in the array of field instrument elements byfactoring one or more measurements obtained from field instrumentsexternal to the array of field instrument elements.
 6. The system ofclaim 1, wherein the filtering operation is performed by one or morefilters, the filters selected from a group consisting of a low-passfrequency filter, a high-pass frequency filter, a band-pass frequencyfilter, a band-stop frequency filter, a median-based non-linear filter,a percentile-based non-linear filter, a weighted averaging filter, awavelet filter, a frequency-domain thresholding filter, a wavelet-domainthresholding filter, a cepstrum-domain thresholding filter, an adaptivelinear filter, and an adaptive non-linear filter.
 7. The system of claim1, the algorithm further comprising computing one or more statisticalindicators from the data and the filtered data; wherein the set ofempirical rules is applied to the data, the filtered data, and the oneor more statistical indicators; and wherein the set of empirical rulesincludes a rule requiring a difference between two or more successivesamples of the data to exceed a threshold value, a rule requiring adifference between two or more successive samples of the filtered datato exceed a threshold value, a rule requiring one or more statisticalindicators of the data to exceed a corresponding threshold value, and arule requiring one or more statistical indicators of the filtered datato exceed a corresponding threshold value; and wherein a threshold valueis selected from the group consisting of: a fixed threshold, a fuzzythreshold, a time-varying threshold, and an adaptive threshold.
 8. Thesystem of claim 1, the processing component further configured to:report incidences of the invalid sample of the data to a user; andnotify a user after the likelihood of a fault exceeds a predefinedthreshold.
 9. The system of claim 1, wherein determining a likelihood ofa fault occurring in the field instrument includes factoring one or moreof: a status of the field instrument, a previously determined likelihoodof a fault for the field instrument, and data collected from other fieldinstruments located proximate to a location of the field instrument. 10.A processing system configured to process data collected from aplurality of field instruments, the processing system comprising: aprocessor; memory operably coupled to the processor; instructions forexecution within the processing system with use of the memory and theprocessor, the instructions configured to: process data originating froma plurality of field instruments; identify invalid samples of the datausing an algorithm, the algorithm configured to detect data changesexceeding a maximum limit within a period of time; correlate invalidsamples of the data to one or more specific field instruments in theplurality of field instruments; and determine a likelihood of a faultoccurring in each of the one or more specific field instruments.
 11. Theprocessing system of claim 10, wherein the algorithm comprises:performing a filtering operation on the data to generate filtered data;computing a mean value M and standard deviation S from the filtereddata; computing an upper control limit and lower control limit using themean value M and standard deviation S; computing one or more statisticalindicators from the data and the filtered data; and identifying theinvalid samples of the data, the invalid samples of the data havingvalues greater than the upper control limit or lower than the lowercontrol limit and satisfying a set of empirical rules applied to thedata, the filtered data, and one or more statistical indicators; whereinthe filtering operation is performed by one or more filters, the filtersselected from a group consisting of: a low-pass frequency filter, ahigh-pass frequency filter, a band-pass frequency filter, a band-stopfrequency filter, a median-based non-linear filter, a percentile-basednon-linear filter, a weighted averaging filter, a wavelet filter, afrequency-domain thresholding filter, a wavelet-domain thresholdingfilter, a cepstrum-domain thresholding filter, an adaptive linearfilter, and an adaptive non-linear filter; and wherein the set ofempirical rules includes a rule requiring a difference between two ormore successive samples of the data to be greater than a deadbandparameter D, a rule requiring a difference between two or moresuccessive samples of the filtered data to be greater than a deadbandparameter, a rule requiring one or more statistical indicators of thedata to be greater than a corresponding maximum threshold, and a rulerequiring one or more statistical indicators of the filtered data to begreater than a corresponding maximum threshold; and wherein thethreshold value is selected from a group consisting of a fixedthreshold, a fuzzy threshold, a time-varying threshold, and an adaptivethreshold.
 12. The processing system of claim 10, wherein determining alikelihood of a fault occurring in each of the one or more specificfield instruments includes factoring one or more of: a status of thespecific field instrument, a previously determined fault likelihood forthe specific field instrument, and a location of the specific fieldinstrument in relation to other instruments in plurality of fieldinstruments.
 13. The processing system of claim 10, wherein at least oneof the specific field instruments comprises an array of field instrumentelements, and wherein the processing component is further configured foridentifying a detected change and the likelihood of fault for a specificelement in the array of field instrument elements by factoring one ormore measurements obtained from field instruments external to the arrayof field instrument elements.
 14. A method comprising: obtaining dataoriginating from a plurality of field instruments; identifying aninvalid sample of the data using an algorithm, the algorithm includingthe steps of: performing a filtering operation on the data to generatefiltered data; computing a mean value M and standard deviation S fromthe filtered data; computing an upper control limit and lower controllimit using the mean value M and standard deviation S; and identifyingthe invalid sample from the filtered data, the invalid sample havingvalues greater than the upper control limit or lower than the lowercontrol limit and satisfying a set of one or more empirical rules;correlating the invalid sample of the data to one or more specific fieldinstruments in the plurality of field instruments; and determining alikelihood of a fault occurring in each of the one or more specificfield instruments from the invalid sample of the data.
 15. The method ofclaim 14, wherein at least one of the specific field instrumentscomprises an array of field instrument elements, and the method furthercomprises: identifying a detected change and the likelihood of fault fora specific element in the array of field instrument elements byfactoring one or more measurements obtained from field instrumentsexternal to the array of field instrument elements.
 16. The method ofclaim 14, wherein the filtering operation is performed by one or morefilters, the filters selected from a group consisting of a low-passfrequency filter, a high-pass frequency filter, a band-pass frequencyfilter, a band-stop frequency filter, a median-based non-linear filter,a percentile-based non-linear filter, a weighted averaging filter, awavelet filter, a frequency-domain thresholding filter, a wavelet-domainthresholding filter, a cepstrum-domain thresholding filter, an adaptivelinear filter, and an adaptive non-linear filter.
 17. The method ofclaim 14, the algorithm further comprising computing one or morestatistical indicators from the data and the filtered data; wherein theset of empirical rules is applied to the data, the filtered data, andthe one or more statistical indicators; and wherein the set of empiricalrules includes a rule requiring a difference between two or moresuccessive samples of the data to exceed a threshold value, a rulerequiring a difference between two or more successive samples of thefiltered data to exceed a threshold value, a rule requiring one or morestatistical indicators of the data to exceed a corresponding thresholdvalue, and a rule requiring one or more statistical indicators of thefiltered data to exceed a corresponding threshold value; and wherein athreshold value is selected from the group consisting of: a fixedthreshold, a fuzzy threshold, a time-varying threshold, and an adaptivethreshold.
 18. The method of claim 14, further comprising one or both ofreporting incidences of the invalid sample of the data to a user, andnotifying a user after the fault likelihood exceeds a predefinedthreshold.
 19. The method of claim 18, wherein the reporting includestransmitting one or more of an error message, specific data status, anindication of an fault of each of the one or more specific fieldinstruments, or a likelihood of a fault of each of the one or morespecific field instruments.
 20. The method of claim 14, whereindetermining a likelihood of a fault occurring in each of the one or morespecific field instruments includes factoring one or more of: a statusof the specific field instrument, a previously determined faultlikelihood for the specific field instrument, and a location of thespecific field instrument in relation to other instruments in pluralityof field instruments.